本課程是数据挖掘專項課程專項課程的一部分

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141 條評論

ChengXiang Zhai

62,831 人已註冊

提供方

数据挖掘專項課程伊利诺伊大学香槟分校

課程信息

16,201 次近期查看

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. 
Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

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第 3 門課程（共 6 門）

数据挖掘專項課程

完成時間大約為33 小時

英語（English）

字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）

您將獲得的技能

Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 3 門課程（共 6 門）

数据挖掘專項課程

完成時間大約為33 小時

英語（English）

講師

授課教師評分

4.5/5 （37 個評分）

ChengXiang Zhai

Professor

Department of Computer Science

88,201 個學生

4 門課程

提供方

伊利诺伊大学香槟分校

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

教學大綱 - 您將從這門課程中學到什麼

內容評分92%（2,909 個評分）

週

週 1

完成時間為 2 小時

Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

完成時間為 2 小時

2 個視頻（總計 15 分鐘）, 5 個閱讀材料, 2 個測驗

完成時間為 4 小時

Week 1

During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).

完成時間為 4 小時

9 個視頻（總計 109 分鐘）, 1 個閱讀材料, 2 個測驗

9 個視頻

1.1 Overview Text Mining and Analytics: Part 111分鐘

1.2 Overview Text Mining and Analytics: Part 211分鐘

1.3 Natural Language Content Analysis: Part 112分鐘

1.4 Natural Language Content Analysis: Part 24分鐘

1.5 Text Representation: Part 110分鐘

1.6 Text Representation: Part 29分鐘

1.7 Word Association Mining and Analysis15分鐘

1.8 Paradigmatic Relation Discovery Part 114分鐘

1.9 Paradigmatic Relation Discovery Part 217分鐘

1 個閱讀材料

Week 1 Overview10分鐘

2 個練習

Week 1 Practice Quiz1小時

Week 1 Quiz1小時

週

週 2

完成時間為 4 小時

Week 2

During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.

完成時間為 4 小時

10 個視頻（總計 116 分鐘）, 1 個閱讀材料, 2 個測驗

10 個視頻

2.1 Syntagmatic Relation Discovery: Entropy11分鐘

2.2 Syntagmatic Relation Discovery: Conditional Entropy11分鐘

2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113分鐘

2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29分鐘

2.5 Topic Mining and Analysis: Motivation and Task Definition7分鐘

2.6 Topic Mining and Analysis: Term as Topic11分鐘

2.7 Topic Mining and Analysis: Probabilistic Topic Models14分鐘

2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110分鐘

2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213分鐘

2.10 Probabilistic Topic Models: Mining One Topic12分鐘

1 個閱讀材料

Week 2 Overview10分鐘

2 個練習

Week 2 Practice Quiz1小時

Week 2 Quiz1小時

週

週 3

完成時間為 10 小時

Week 3

During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.

完成時間為 10 小時

10 個視頻（總計 103 分鐘）, 2 個閱讀材料, 3 個測驗

10 個視頻

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models12分鐘

3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110分鐘

3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28分鐘

3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111分鐘

3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210分鐘

3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36分鐘

3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110分鐘

3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210分鐘

3.9 Latent Dirichlet Allocation (LDA): Part 110分鐘

3.10 Latent Dirichlet Allocation (LDA): Part 212分鐘

2 個閱讀材料

Week 3 Overview10分鐘

Programming Assignments Overview10分鐘

2 個練習

Week 3 Practice Quiz1小時

Quiz: Week 3 Quiz1小時

週

週 4

完成時間為 5 小時

Week 4

During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.

完成時間為 5 小時

9 個視頻（總計 141 分鐘）, 1 個閱讀材料, 2 個測驗

9 個視頻

4.1 Text Clustering: Motivation15分鐘

4.2 Text Clustering: Generative Probabilistic Models Part 116分鐘

4.3 Text Clustering: Generative Probabilistic Models Part 28分鐘

4.4 Text Clustering: Generative Probabilistic Models Part 314分鐘

4.5 Text Clustering: Similarity-based Approaches17分鐘

4.6 Text Clustering: Evaluation10分鐘

4.7 Text Categorization: Motivation14分鐘

4.8 Text Categorization: Methods11分鐘

4.9 Text Categorization: Generative Probabilistic Models31分鐘

1 個閱讀材料

Week 4 Overview10分鐘

2 個練習

Week 4 Practice Quiz1小時

Week 4 Quiz1小時

週

週 5

完成時間為 4 小時

Week 5

During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).

完成時間為 4 小時

7 個視頻（總計 121 分鐘）, 1 個閱讀材料, 2 個測驗

7 個視頻

5.1 Text Categorization: Discriminative Classifier Part 120分鐘

5.2 Text Categorization: Discriminative Classifier Part 231分鐘

5.3 Text Categorization: Evaluation Part 114分鐘

5.4 Text Categorization: Evaluation Part 210分鐘

5.5 Opinion Mining and Sentiment Analysis: Motivation17分鐘

5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification11分鐘

5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression13分鐘

1 個閱讀材料

Week 5 Overview10分鐘

2 個練習

Week 5 Practice Quiz1小時

Week 5 Quiz1小時

週

週 6

完成時間為 4 小時

Week 6

During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.

完成時間為 4 小時

8 個視頻（總計 120 分鐘）, 1 個閱讀材料, 2 個測驗

8 個視頻

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 115分鐘

6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 214分鐘

6.3 Text-Based Prediction12分鐘

6.4 Contextual Text Mining: Motivation6分鐘

6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis17分鐘

6.6 Contextual Text Mining: Mining Topics with Social Network Context14分鐘

6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision19分鐘

6.8 Course Summary18分鐘

1 個閱讀材料

Week 6 Overview10分鐘

2 個練習

Week 6 Practice Quiz1小時

Week 6 Quiz1小時

審閱

4.5

141 條評論

5 stars
67.28%
4 stars
20.83%
3 stars
8.17%
2 stars
1.85%
1 star
1.85%

來自文本挖掘和分析的熱門評論

由 JH 提供2017年2月9日

Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.

由 DC 提供2018年3月24日

The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.

由 AC 提供2017年2月11日

This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.

由 JS 提供2017年6月6日

The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!

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關於数据挖掘專項課程

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.

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